Bibliographic record
Abstract
In this survey, we present many results about quasi-cluster algebras. In the first section, we present a comprehensive study of non-oriented surfaces, focusing on their triangulations and arc flips. Drawing from Wilson's work \[Discrete Comput. Geom. 59 (2018), 680–706], we analyze properties of the cluster complex. Next, we provide the definition of quasi-cluster algebras, originally introduced by Dupont and Palesi, and highlight some of their properties, notably their connection to the cluster categories. Furthermore, we present Wilson's formula \[arXiv:1912.12789v1] for directly computing cluster variables, bypassing the need for recursion. Finally, we introduce Wilson's alternative definition \[Int. Math. Res. Not. IMRN (2018), 3800–3833, and Selecta Math. (N.S.) 26 (2020), article no. 72], which endows these algebras with Laurent phenomenon algebra properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".